{"id":"W4411019938","doi":"10.1109/tmtt.2025.3571574","title":"A Novel Neuro-TF Modeling Technique Incorporating Parametric Sanathanan–Koerner Iteration of Continuous Pole/Residue Extraction","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Residue (chemistry); Parametric statistics; Extraction (chemistry); Control theory (sociology); Computer science; Biological system; Electronic engineering; Engineering; Mathematics; Chromatography; Chemistry; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003776962,0.0002003838,0.0002226229,0.000407639,0.0001356094,0.00003903871,0.00008186233,0.0001664308,0.000005563883],"category_scores_gemma":[0.00002432991,0.0002088465,0.00005965138,0.0005091641,0.00005298358,0.0002800356,0.000002332213,0.0003263249,3.319923e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003843767,"about_ca_system_score_gemma":0.00001782385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002192812,"about_ca_topic_score_gemma":0.00001588298,"domain_scores_codex":[0.9991254,0.00005238066,0.0003599779,0.0002290289,0.00007729878,0.000155926],"domain_scores_gemma":[0.9994254,0.0001917707,0.00008599215,0.0001702616,0.0000968726,0.00002973454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009543989,0.00007123715,0.000003572468,0.0001526045,0.00003017966,0.000001093098,0.0001349764,0.1974126,0.7272516,0.00368714,0.000005024504,0.07115446],"study_design_scores_gemma":[0.0001607987,0.00007806679,0.000001378026,0.0002233082,0.00004215731,0.00001837541,0.0001243706,0.07518044,0.9142606,0.009710646,0.00002494486,0.0001748784],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02903854,0.0002000313,0.9689398,0.00001505904,0.0001030429,0.0003857113,0.00002167003,0.0005473499,0.0007487422],"genre_scores_gemma":[0.9219271,0.0002660045,0.07753901,0.00005430596,0.0000131394,0.0001068138,0.000005828581,0.00003193455,0.00005587546],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8928885,"threshold_uncertainty_score":0.8516512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007775897885926136,"score_gpt":0.2468597778972549,"score_spread":0.2390838800113287,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}